jam_wrap: Wrapper around 'JAM'
In simisc/cojam: Multi-variant colocalisation with summary genetic association data

Description Usage Arguments Value

View source: R/functions.R

Constructs a collated list of data that can be passed to JAM via do.call, or as an argument to cojam. The prior proportion of causal covariates is treated as unknown and given a Beta(a, b) hyper-prior.

jam_wrap(
  marginal_beta,
  snp_names,
  ref_genotypes,
  n,
  trait_variance,
  binary_outcome = FALSE,
  marginal_beta_se = NULL,
  bb_prior_a = 1,
  bb_prior_b = NULL,
  ...
)

`marginal_beta`	Vector of marginal effect estimates to re-analyse with JAM under multivariate models. For GWAS summaries, these or log-ORs.
`snp_names`	SNP identifiers (e.g. RSID), in the same order as `marginal_beta`.
`ref_genotypes`	Reference genotype matrix used by JAM to impute the SNP-SNP correlations. Genotypes must be coded as a numeric risk allele count 0/1/2. Non-integer values reflecting imputation may be given. NB: The risk allele coding must correspond to that used in `marginal.betas`. Must be positive definite, with SNP identifiers in the column names.
`n`	The size of the dataset in which the summary statistics `marginal.betas` were calculated
`trait_variance`	Estimate of the trait (outcome) variance.
`binary_outcome`	Is the trait (outcome) binary? Should be `TRUE` for GWAS or `FALSE` for QTL studies.
`marginal_beta_se`	Only required if the trait (outcome) is binary: standard errors of the log-ORs.
`bb_prior_a`	Parameter of Beta(a, b) prior on proportion of causal covariates, default 1.
`bb_prior_b`	Parameter of Beta(a, b) prior on proportion of causal covariates. Default is the number of SNPs that are common to both `snp_names` and `colnames(ref_genotypes)`. Higher values of `bb_prior_b` relative to `bb_prior_a` will encourage greater sparsity. Use `GetBetaBinomParams` for suggested beta-binomial parameters.
`...`	Other arguments to `JAM`.